Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computerized method for determining treatment for a patient having congestive heart failure and at least one other medical condition, the method comprising: receiving, by one or more computer processing components, patient-results information for a patient from a patient information database, wherein the patient information database is remote from at least one of the one or more computer processing components; extract, by the one or more computer processing components, discrete patient data from the patients-results information; receiving, by the one or more computer processing components, one or more rules from a parameters database, wherein the parameters database is remote from the one or more computer processing components, and wherein the one or more rules comprise a rule relevant to the discrete patient data; based on said discrete patient data and the one or more rules, determining, by a first processing component of the one or more computer processing components, whether said patient-results information suggests a trigger event; upon a determination that said trigger event has occurred, determining by a second processing component of the one or more computer processing components, at least one goal based on said trigger event, wherein the at least one goal is communicated to the one or more computer processing components by the parameters database; based on said goal, selecting, by the one or more computer processing components, a first plan, from a library of plans, corresponding to said determined goal, wherein the library of plans is communicated to the one or more computer processing components by the parameters database; in response to selecting the first plan, receiving additional patient-results information specified by said plan, wherein the additional patient-results information is communicated to the one or more computer processing components by the patient information database; and executing said first plan, wherein said execution comprises: (1) from among a library of solvers, determining a first solver to determine patient conditions and recommended treatments, said first solver comprising a finite state machine; (2) receiving first-solver parameters for said first solver, wherein said first solver is running on the one or more computer processing components; (3) preparing patient-results information for said first solver; (4) instantiating said first solver based on said prepared patient-results information and said first-solver parameters; (5) applying said first solver to determine said patient conditions and recommended treatments, each patient condition comprising an evaluated state for the patient condition; (6) determining that a second solver is needed to assist said first solver to determine patient conditions and recommended treatments, wherein the second solver is running on the one or more computer processing components; (7) invoking the second solver to assist said first solver to determine patient conditions and recommended treatments, said second solver comprising a mixed-integer linear solver, said second solver being invoked by the first solver; (8) communicating the evaluated states for the determined said patient conditions from the first solver to the second solver; (9) preparing patient-results information for said second solver; (10) instantiating said second solver based on said prepared patient-results information and said evaluated states; (11) based on said determined patient conditions and recommended treatments, communicating actions and dispositions specific to said patient from the one or more computer processing components to a second computer processing component, wherein the second computer processing component is remote from the one or more computer processing components; and (12) displaying the communicated actions and dispositions specific to said patient on a user interface of the second computer processing component.
This invention relates to a computerized system for determining personalized treatment plans for patients with congestive heart failure and at least one additional medical condition. The system addresses the challenge of managing complex, multi-condition patient care by integrating patient data, clinical rules, and advanced computational solvers to generate tailored treatment recommendations. The method begins by retrieving patient data from a remote database, extracting relevant clinical information, and applying predefined rules to identify trigger events that warrant intervention. Upon detecting a trigger, the system determines treatment goals and selects a corresponding care plan from a predefined library. The system then collects additional patient data as specified by the plan and executes a multi-stage solver process. A finite state machine solver first evaluates patient conditions and recommends treatments, generating evaluated states for each condition. If further analysis is needed, a mixed-integer linear solver is invoked to assist, receiving the evaluated states and refining the recommendations. The final treatment actions and dispositions are then communicated to a remote display system for clinical review. This approach leverages distributed computing, rule-based decision-making, and advanced optimization techniques to improve treatment precision for patients with complex medical histories. The system dynamically adapts to patient-specific data and clinical triggers, ensuring personalized care recommendations.
2. The computerized method of claim 1 , wherein said actions and dispositions specific to said patient are determined by invoking an expert rules engine, and wherein said determined actions and dispositions include at least one of an order, patient condition, recommended treatment, recommended additional testing, or recommended execution of a second plan.
This invention relates to a computerized method for personalized patient care, addressing the challenge of providing tailored medical actions and dispositions based on individual patient data. The method involves analyzing patient-specific information to generate customized recommendations, which may include medical orders, assessments of patient conditions, suggested treatments, additional diagnostic tests, or the execution of secondary care plans. A key aspect of the method is the use of an expert rules engine to determine these actions and dispositions. The rules engine applies predefined medical knowledge and logic to the patient data, ensuring that the recommendations are evidence-based and contextually relevant. This approach enhances clinical decision-making by reducing variability and improving the accuracy of patient-specific interventions. The system aims to streamline workflows for healthcare providers while ensuring that each patient receives the most appropriate care based on their unique medical profile. By automating the analysis and recommendation process, the method supports more efficient and consistent clinical practices, ultimately improving patient outcomes.
3. The computerized method of claim 1 , wherein said determined actions and dispositions specific to said patient are processed for presentation.
This invention relates to a computerized method for processing and presenting patient-specific actions and dispositions in a healthcare system. The method addresses the challenge of efficiently managing and delivering personalized medical recommendations, treatments, and follow-up actions for individual patients based on their unique medical conditions, history, and data. The method involves analyzing patient data, such as medical records, diagnostic results, and treatment history, to determine appropriate actions and dispositions tailored to the patient. These actions may include treatment plans, medication prescriptions, diagnostic tests, or follow-up appointments. The method then processes these determined actions and dispositions for presentation to healthcare providers or patients, ensuring that the information is organized, accessible, and actionable. The presentation may involve generating reports, alerts, or notifications that highlight critical information, prioritize tasks, or provide decision support. The method may also integrate with electronic health record (EHR) systems, clinical decision support tools, or patient portals to streamline workflows and improve care coordination. By automating the processing and presentation of patient-specific actions, the method enhances efficiency, reduces errors, and ensures that healthcare providers have the necessary information to deliver personalized and timely care.
4. The computerized method of claim 3 , wherein said determined actions and dispositions processed for presentation further includes functionality for presenting information enabling a health-care provider to see which plan, of said library of plans, was executed and information associated with said plan including said trigger event, patient-results information, and goals.
This invention relates to a computerized method for managing healthcare plans, specifically for tracking and presenting the execution of treatment plans to healthcare providers. The system addresses the challenge of ensuring healthcare providers have visibility into which treatment plans from a library of predefined plans were executed for a patient, along with associated details such as the trigger event that initiated the plan, patient results, and treatment goals. The method involves processing and presenting information that allows healthcare providers to review the specific plan executed, including the conditions or events that triggered its activation. Additionally, it provides patient-results information, which may include outcomes, progress, or other relevant data generated during the execution of the plan. The system also displays the goals associated with the plan, enabling providers to assess whether the treatment objectives were met. This functionality enhances transparency and accountability in healthcare delivery by ensuring that providers can track the execution of treatment plans and evaluate their effectiveness. The system may be part of a broader healthcare management platform that includes a library of predefined plans, automated triggers, and data collection mechanisms to support clinical decision-making and patient care.
5. The computerized method of claim 1 , wherein the determination of at least one of said trigger event, goal, or plan selection are performed by a software agent.
This invention relates to a computerized method for automated decision-making in software systems, specifically addressing the challenge of dynamically determining trigger events, goals, and plan selections without manual intervention. The method leverages a software agent to autonomously assess and execute these determinations, enhancing efficiency and adaptability in complex systems. The software agent operates by analyzing system conditions, user inputs, or predefined criteria to identify trigger events that initiate subsequent actions. It also evaluates system objectives to define or adjust goals, ensuring alignment with operational requirements. Additionally, the agent selects or modifies plans—sequences of actions designed to achieve the established goals—based on real-time data or predefined rules. The agent's autonomous decision-making reduces the need for manual oversight, improving responsiveness and scalability in dynamic environments. This approach is particularly useful in applications requiring real-time adjustments, such as autonomous systems, process automation, or adaptive software frameworks. By delegating trigger event detection, goal formulation, and plan selection to a software agent, the method minimizes latency and human error while optimizing system performance. The agent's capabilities may include machine learning, rule-based logic, or hybrid approaches to refine decision-making over time. The invention enhances automation in scenarios where rapid, context-aware decisions are critical.
6. The computerized method of claim 1 , wherein the determination of at least one of said trigger event, goal, or plan selection are performed using logic comprising at least one of a rules engine, a Boolean evaluation, or a rules-based determination.
This invention relates to a computerized method for decision-making in automated systems, particularly for determining trigger events, goals, or plan selections. The method addresses the challenge of efficiently and accurately processing complex decision logic in dynamic environments where real-time or near-real-time responses are required. The system evaluates conditions, goals, or plans using a rules engine, Boolean evaluation, or other rules-based determination to ensure consistent and predictable outcomes. The rules engine applies predefined logic to assess input data against stored criteria, while Boolean evaluation simplifies decision-making by reducing conditions to true or false outcomes. Rules-based determination allows for structured, hierarchical decision-making, where multiple conditions are evaluated in sequence or parallel to derive a final result. This approach enhances automation by reducing reliance on manual intervention and improving system responsiveness. The method is particularly useful in applications such as process automation, workflow management, and adaptive control systems where deterministic decision-making is critical. By leveraging structured logic, the system ensures that decisions align with predefined rules, improving reliability and consistency in automated operations.
7. A decision support system determining treatment for a patient having congestive heart failure and at least one other medical condition, comprising: a library of healthcare agent solvers configured for evaluating patient information to facilitate clinical decision support for the patient; one or more computer processors; and one or more computer storage media storing computer-useable instructions that, when executed by the one or more processors, implement a method comprising: receiving, by one or more computer processing components, patient information for the patient from a patient information database, wherein the patient information database is remote from at least one of the one or more computer processing components; extract, by the one or more computer processing components, discrete patient data from the patients-results information; receiving, by the one or more computer processing components, one or more rules from a parameters database, wherein the parameters database is remote from the one or more computer processing components, and wherein the one or more rules comprise a rule relevant to the discrete patient data; based on the discrete patient data, determining, by a first processing component of the one or more computer processing components, two or more clinical conditions of the patient, at least one clinical condition including heart failure; based on the two or more clinical conditions, determining, by the first processing component of the one or more computer processing components a set of solver-content parameters; from a library of solvers, using the set of solver-content parameters, generating a finite state machine running on the one or more computer components having states and transition-rules corresponding to the patient information and solver-content parameters; determining that a second solver is needed to assist said finite state machine to determine the conditions and a recommended treatment for the patient wherein the second solver is running on the one or more computer processing components; invoking, by said finite state machine, a linear solver to assist said finite state machine to determine the conditions and the recommended treatment for the patient; evaluating the finite state machine to determine the conditions and a recommended treatment for the patient, each state being evaluated using the linear solver, the finite state machine passing states to the linear solver; based on the determined patient conditions and recommended treatment, communicating actions and dispositions specific to said patient from the one or more computer processing components to a second computer processing component, wherein the second computer processing component is remote from the one or more computer processing components; and displaying the communicated actions and dispositions specific to said patient on a user interface of the second computer processing component.
This system provides a decision support tool for treating patients with congestive heart failure and at least one additional medical condition. The system integrates patient data from remote databases, including clinical records and treatment parameters, to analyze complex medical conditions. A library of healthcare agent solvers evaluates the patient's information to generate a finite state machine that models the patient's conditions and potential treatments. The system dynamically selects and invokes additional solvers, such as linear solvers, to refine treatment recommendations. The finite state machine processes patient data through defined states and transition rules, ensuring comprehensive evaluation. The system then communicates personalized treatment actions and dispositions to a remote interface, where healthcare providers can view the recommendations. This approach improves clinical decision-making by leveraging automated, data-driven analysis of multi-condition patient cases.
8. The decision support system of claim 7 , wherein said action or disposition is determined by invoking an expert rules engine, and wherein said determined action or disposition includes at least one of an order, patient condition, recommended treatment, recommended additional testing.
A decision support system assists healthcare professionals by analyzing patient data to determine appropriate actions or dispositions for patient care. The system processes patient information, such as medical history, symptoms, and test results, to generate recommendations. These recommendations may include specific medical orders, assessments of patient conditions, suggested treatments, or recommendations for additional diagnostic testing. The system uses an expert rules engine to evaluate the patient data against predefined medical guidelines and protocols. The rules engine applies logical decision-making processes to derive the most suitable action or disposition based on the input data. This automated analysis helps clinicians make informed decisions, reduces diagnostic errors, and improves patient outcomes by ensuring adherence to best practices. The system is designed to integrate with existing healthcare information systems, providing real-time support during patient care. By leveraging structured medical knowledge, the system enhances efficiency and consistency in clinical decision-making.
9. The decision support system of claim 8 , wherein said determined action or disposition specific to said patient are processed for presentation.
A decision support system provides personalized medical recommendations by analyzing patient data to determine specific actions or dispositions tailored to individual patients. The system processes and presents these recommendations in a user-friendly format, enabling healthcare providers to make informed decisions. The system integrates patient-specific data, such as medical history, current symptoms, and diagnostic results, to generate context-aware suggestions. These suggestions may include treatment options, diagnostic procedures, or follow-up actions. The system ensures that the recommendations are relevant and actionable by filtering and prioritizing information based on the patient's unique profile. By presenting the determined actions or dispositions clearly, the system enhances clinical decision-making efficiency and reduces the risk of errors. The system may also incorporate guidelines, best practices, and evidence-based medicine to ensure recommendations align with current medical standards. This approach supports healthcare providers in delivering personalized and effective care while optimizing workflow and resource utilization.
10. The decision support system of claim 7 , wherein said determination of an indication of heart failure is performed by a software agent.
A decision support system is designed to assist in the diagnosis and management of heart failure by analyzing patient data. The system includes a software agent that processes physiological and clinical data to determine whether a patient exhibits indications of heart failure. The software agent evaluates inputs such as cardiac function metrics, biomarkers, and patient history to generate a risk assessment or diagnostic output. This automated analysis helps clinicians identify heart failure earlier and with greater accuracy, reducing reliance on manual interpretation and improving patient outcomes. The system may integrate with electronic health records and other medical devices to collect and analyze real-time data, enhancing its diagnostic capabilities. By leveraging machine learning or rule-based algorithms, the software agent can adapt to new data and refine its predictions over time, ensuring continuous improvement in diagnostic performance. The system aims to streamline clinical workflows, reduce diagnostic errors, and support personalized treatment decisions for heart failure patients.
11. The decision support system of claim 7 , wherein said determination of an indication of heart failure is performed using logic comprising at least one of a rules engine, a Boolean evaluation, or a rules-based determination.
The invention relates to a decision support system for detecting heart failure. The system monitors physiological data from a patient, such as heart rate, blood pressure, or other relevant metrics, to identify potential indicators of heart failure. The system processes this data using a rules-based approach to determine whether the patient's condition suggests heart failure. The rules engine, Boolean evaluation, or other rules-based logic evaluates predefined criteria to assess the likelihood of heart failure. If the system detects a pattern or threshold consistent with heart failure, it generates an alert or recommendation for further medical intervention. The system may integrate with existing medical devices or electronic health records to enhance diagnostic accuracy and provide timely support to healthcare providers. The rules-based logic ensures consistent and standardized evaluation of patient data, reducing variability in decision-making. This approach helps clinicians quickly identify high-risk patients and take appropriate action, improving patient outcomes. The system may also adapt its rules over time based on new medical guidelines or patient-specific data to maintain relevance and accuracy.
12. A method of determining treatment for a patient having congestive heart failure, the method comprising: receiving, by one or more computer processing components, patient-results data, associated with a patient from a patient information database, wherein the patient information database is remote from at least one of the one or more computer processing components; based on said discrete patient data, determining discrete patient data operable for use by a finite state machine solver and a mixed-integer linear solver, wherein the finite state machine solver and the mixed-integer linear solver are running on the one or more computer processing components; determining whether said discrete patient data is indicative of heart failure; based on said determination of whether said patient information is indicative of heart failure, accessing heart-failure content parameters operable for use by a finite state machine solver from a memory associated with a parameters database, the parameters database being remote from the one or more computer processing components; based on said received content parameters and said discrete patient data, instantiating the finite state machine solver; determining that the mixed-integer linear solver is needed to assist said finite state machine solver to determine patient condition or recommended treatment; invoking, by said finite state machine solver, the mixed-integer linear solver to assist said finite state machine solver to determine patient condition or recommended treatment; using the finite state machine solver and the mixed-integer linear solver, determining a patient condition or recommended treatment, based on said discrete patient data and said solver-content parameters, said finite state machine solver passing states to said mixed integer linear solver; based on said determined patient condition or recommended treatment, applying a rules engine to determine an action; and initiating the action.
This invention relates to a computer-implemented method for determining treatment for patients with congestive heart failure. The method addresses the challenge of analyzing complex patient data to provide accurate and actionable medical recommendations. The system receives patient data from a remote database, processes it into a format suitable for computational analysis, and determines whether the data indicates heart failure. If heart failure is detected, the system retrieves heart-failure-specific parameters from a remote parameters database. These parameters are used to instantiate a finite state machine solver, which evaluates the patient's condition. The finite state machine solver may invoke a mixed-integer linear solver to assist in determining the patient's condition or recommended treatment, with the solvers exchanging state information to refine the analysis. The system then applies a rules engine to the results to determine an appropriate action, such as a treatment recommendation, and initiates that action. The method leverages both finite state machine and mixed-integer linear solvers to handle the complexity of medical decision-making, ensuring that patient data is analyzed comprehensively and treatment recommendations are based on rigorous computational models.
13. The method of claim 12 , wherein said finite state machine is instantiated such that states and transition-rules for said finite state machine are determined based on said discrete patient data and said solver-content parameters, and wherein said each state of said finite state machine is evaluated using said mixed-integer linear solver.
This invention relates to a system for generating and evaluating a finite state machine (FSM) to model patient data and solver-content parameters in a medical or healthcare context. The FSM is dynamically instantiated, where its states and transition rules are determined based on discrete patient data and solver-content parameters. Each state of the FSM is evaluated using a mixed-integer linear solver, which optimizes or analyzes the state transitions and outcomes. The system processes patient data, which may include medical records, diagnostic results, or treatment history, and uses solver-content parameters to define constraints, objectives, or other rules governing the FSM. The mixed-integer linear solver assesses each state to determine feasible transitions, optimal paths, or decision outcomes, enabling automated decision support or predictive modeling in healthcare applications. The FSM may represent clinical pathways, treatment protocols, or diagnostic workflows, where transitions between states correspond to changes in patient conditions, interventions, or outcomes. The solver ensures that transitions adhere to medical guidelines, resource constraints, or other predefined criteria, improving the reliability and efficiency of patient management. This approach allows for adaptive, data-driven decision-making in healthcare by integrating discrete patient data with formal optimization techniques.
14. The method of claim 13 , wherein said determined action or disposition is processed for presentation.
A system and method for processing and presenting determined actions or dispositions in a data processing environment. The invention addresses the challenge of efficiently managing and displaying actions or dispositions derived from data analysis, ensuring timely and accurate decision-making. The method involves receiving input data, analyzing the data to identify relevant patterns or conditions, and determining an appropriate action or disposition based on predefined criteria. Once the action or disposition is determined, it is processed for presentation to a user or another system. This processing may include formatting the output, prioritizing the information, or integrating it with other data sources to enhance clarity and usability. The system ensures that the presented information is actionable, reducing the time and effort required for manual review and decision-making. The invention is particularly useful in applications such as fraud detection, customer service automation, and real-time monitoring systems where rapid and accurate responses are critical. By automating the analysis and presentation of actions or dispositions, the system improves efficiency and reduces errors in decision-making processes.
15. The method of claim 12 , wherein said determination of an indication of heart failure is performed by a software agent.
This invention relates to a method for detecting heart failure using a software agent. The method involves analyzing physiological data from a patient to determine whether the patient is experiencing heart failure. The software agent processes the physiological data, which may include measurements such as heart rate, blood pressure, or other relevant indicators, to assess the patient's condition. The software agent applies predefined criteria or algorithms to evaluate the data and generates an output indicating whether heart failure is present. The method may also involve additional steps, such as collecting the physiological data from medical devices or sensors, storing the data for analysis, and providing the results to healthcare providers or the patient. The software agent may operate autonomously or in conjunction with other systems to enhance the accuracy and reliability of the heart failure determination. The invention aims to improve early detection and monitoring of heart failure, enabling timely medical intervention and better patient outcomes.
16. The method of claim of 12 , wherein said determination of an indication of heart failure is performed using logic comprising at least one of a rules engine, a Boolean evaluation, or a rules-based determination.
This invention relates to methods for detecting heart failure using computational logic. The method involves analyzing physiological data, such as heart rate, blood pressure, or other relevant metrics, to assess whether a patient may be experiencing heart failure. The core innovation lies in the use of a logic-based approach to evaluate this data, which can include a rules engine, Boolean evaluation, or other rules-based determination techniques. These methods systematically apply predefined criteria to the input data to generate an indication of heart failure, enabling early detection and intervention. The rules engine may incorporate medical guidelines or empirical thresholds to assess the likelihood of heart failure, while Boolean evaluation simplifies the decision-making process by applying true/false conditions to the data. This approach ensures objective and consistent analysis, reducing reliance on subjective interpretation. The method may be integrated into medical monitoring systems, wearable devices, or clinical decision-support tools to improve diagnostic accuracy and patient outcomes. By leveraging structured logic, the system provides a reliable mechanism for identifying potential heart failure cases, supporting timely medical intervention.
17. A system for facilitating clinical decision making, comprising: patient information related to a patient; solver-content parameters operable for use to instantiate one or more solvers, from a library of solvers, for use to determine at least one condition or recommended treatment associated with said patient; a data-extraction solver operable to determine discrete patient data from said patient information; a condition and recommendation resolver agent (“resolver agent”) operable to receive said discrete patient data and said solver-content parameters, and based on said discrete data and solver-content parameters, determine at least one condition or recommended treatment associated with said patient; and an expert rules engine operable for receiving at least one condition or recommended treatment and determining an action or disposition based on said at least one condition or recommended treatment; (1) said resolver agent further comprising a generic finite state machine and a mixed-integer linear solver; (2) said resolver agent being further operable to instantiate and execute a patient-specific finite state machine, based on said generic finite state machine, having states and transition-rules corresponding to said discrete patient data and said solver-content parameters, the patient-specific finite state machine determining that a second solver is needed to assist said first patient-specific finite state machine and invoking the mixed-integer linear solver to evaluate each state of the patient-specific finite state machine to determine the at least one condition or recommended treatment; and (3) said resolver agent being further operable to use said mixed integer linear solver to evaluate each state of the finite state machine.
The system is designed to assist clinical decision-making by analyzing patient data and generating conditions or treatment recommendations. It addresses the challenge of integrating diverse patient information and applying advanced computational methods to derive actionable medical insights. The system includes patient information, solver-content parameters for selecting and configuring solvers from a library, and a data-extraction solver to process patient data into discrete elements. A resolver agent receives this data and parameters, using a generic finite state machine and a mixed-integer linear solver to determine conditions or treatments. The resolver agent dynamically instantiates a patient-specific finite state machine, which may invoke additional solvers, such as the mixed-integer linear solver, to evaluate states and transitions based on the patient's data and parameters. The system also includes an expert rules engine that processes the conditions or recommendations to determine specific actions or dispositions. The resolver agent's finite state machine and solver integration enable adaptive, data-driven decision support for clinical workflows.
18. The system of claim 17 , wherein the generic finite state machine returns an actual state for each clinical condition of the patient, the actual states being passed to the mixed integer linear solver as parameters, to apply the mixed integer solver based on a clinical state, and content tables.
This invention relates to a healthcare system that uses a generic finite state machine and a mixed integer linear solver to manage patient conditions. The system addresses the challenge of dynamically adapting clinical decision-making based on real-time patient data and evolving medical conditions. The finite state machine monitors and evaluates patient conditions, generating an actual state for each clinical condition. These states are then passed as parameters to a mixed integer linear solver, which processes the data in conjunction with predefined clinical states and content tables. The solver optimizes treatment decisions by considering constraints and objectives derived from the patient's current clinical state. The system ensures that medical interventions are tailored to the patient's specific needs, improving treatment accuracy and efficiency. The integration of the finite state machine and the solver allows for real-time adjustments, enhancing the system's ability to handle complex and dynamic clinical scenarios. This approach reduces the risk of errors and ensures that decisions align with the latest patient data and medical guidelines.
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October 1, 2019
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